Recognizing the Activity Daily Living (ADL) for Subject Independent
M.N.Shah Zainudin1, Y.J. Kee2, M.I. Idris3, M.R. Kamaruddin4, R.H. Ramlee5
1M.N.Shah Zainudin, Cetri, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia.
2Y.J.Kee, Cetri, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia.
3M.I.Idris, Cetri, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia.
4M.R.Kamaruddin, Cetri, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia.
5R.H.Ramlee, Cetri, Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malacca, Malaysia.
Manuscript received on 11 August 2019. | Revised Manuscript received on 14 August 2019. | Manuscript published on 30 September 2019. | PP: 5422-5427 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2381078219/2019©BEIESP | DOI: 10.35940/ijrte.B2381.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, Human Activity Recognition (HAR) has gained meaningful information for a human being. A wearable sensor like an accelerometer, small and simple to perform, has opened the room for scientists to explore an initial understanding of ubiquitous computing. The wearable sensor has begun to receive attention among researchers in some respects to conduct their studies in a wide area of recognition of human activity. Recent ADL discusses not only simple activities but also cater to the broad categories of complex activities. However, when involving enormous numbers of a subject, the accuracy of recognition tends to reduce. Although a different subject performed the same activity, the acceleration signal acquired considerably differs. This is due to the distinct pattern of action for each subject based on various factors such as subject age, gender, emotion and personality. Thus, by enhancing the accuracy of recognition of ADL, this article proposes the framework for addressing the subject independent matter. The signal acquired from an accelerometer sensor will undergo a segmentation process to extract important features. Some of the characteristics may be meaningless in some instances to determine the class. Therefore, proposing a variety of features to select the most relevant features that can lead to accuracy above 90%. Also, this article outlined a brief empirical evaluation of previous related work. Using several machine learning algorithms, this preliminary work will be examined and analyzed.
Index Terms: Activity Daily Living (ADL), Accelerometer, Wearable Sensor, Machine Learning.
Scope of the Article: Machine Learning